Hi, everyone, and welcome. I'm really excited to be here today to talk to you about vector search. It's a fascinating technology that's transforming the way we find information in the age of semantic understanding, where we're not just looking for keywords, but actual meaning. this comprehensive review. we'll give you an idea of the architectural foundations of vector search and its implementation across multiple domains. my name is Siddharth Pratap Singh. All right. to fully appreciate vector search, let's take a quick look back at how search has evolved. Remember when we used to rely on typing in exact keywords and crossing our fingers? That was token based search, using simple word matching. It was fine for basic searches, but it often struggled with the complexities and ambiguities of human language. Then came semantic search, a step in the right direction. The idea was to understand the intent and context behind our words, but the technology at the time just wasn't up to the task. Fast forward to today and we have vector search, powered by incredible advances in machine learning and NLP. It's like semantic search on a whole new level, able to truly understand what we mean and find relevant results even in massive, messy data sets. Okay, let's dive into the mechanics of vector search. At its core, it's about transforming text into high dimensionality vectors. These vectors are like numerical representations that capture the meaning of words and their relationships to one another. Think of it as converting language into a format that computers can really grasp, a way to map words and concepts in a multi dimensional space. This transformation is made possible by neural networks, sophisticated algorithms that can process complex data relationships. They can handle all the nuances of human language, like synonyms, ambiguity, and context, while maintaining efficiency. Different types of neural networks are used in the process. each with its own strengths. Some popular ones include RNNs, Recurrent Neural Networks, Convolutional Neural Networks, and Transformer Networks. RNNs are practically good at processing sequential data like text, while CNNs excel at capturing local patterns. Transformers have revolutionized NLP with their ability to handle long range dependencies in text. And to find the information we need amongst a sea of data, we use specialized index structures. these index structures are designed to enable rapid similarity searches in high dimensional spaces. They allow us to quickly sift through millions of vectors and pinpoint the ones that are most similar to our search queries, all while balancing search speed, memory efficiency, and accuracy. Some common index structures used in embedding based search or vector search include KD trees, bird trees, locality sensitive hashing, which is LSH. Okay, so let's go with the implementation architecture. We'll take a closer look at how a vector search system is actually implemented. There are a few key components that work together seamlessly. First, we have the document processing pipeline. This pipeline takes raw text data. And transforms it into a machine readable vectors we were just, referring to. This involves several steps, including cleaning the text, breaking it into meaningful chunks, like sentences or paragraphs, and then creating those rich numerical representations into, through our chosen embedding model into vectors. This process can also include things like stemming, lemmatization, and stop word removal to further optimize the search performance. Then, when you type in a search query, the system actually converts it into the same vector format as the documents. The dimensionality of the search, the vectors from the search queries as well as the documents are the same. It's like translating your query into the language of the data, enabling true semantic matching. Finally, the index. The search index comes into play. It uses efficient nearest neighbor algorithms to quickly find the documents that are most similar to your query based on their vector representations. how, what we mean by similar is that in that dimensional space, the documents that are most relevant or, answer the question that your query has, they lie closer in that dimensional space. The system then intelligently ranks the results. taking into account not only the semantic relevance, but also the factors like quality of the document, the historical behavioral popularity of the document, amongst the users on your platforms, and then some user preferences. Okay, so we'll go over some domain specific applications. We'll take up e commerce and health care. So embedding base search or vector search isn't just a theoretical concept. It's already having a real world impact in various fields. In e commerce, it's revolutionizing the way we shop online. It helps us find the perfect product, the most relevant product, even if we can't quite articulate what we are looking for. It can understand the intent behind vague searches. For example, something like, birthday gift for a 10 year old who likes science and suggests relevant products. on the business side, it helps companies detect fraud and ensure secure transactions. for queries like this, for example, the one I mentioned, birthday gift for a 10 year old who likes science. Most of the products that an e commerce catalog has. It does not contain keywords that go along with it, right? People will put in science kits, astronomy kits, or computer kits as a product, but they wouldn't necessarily mention 10 year olds who like science. So these queries are very well represented. And the products, are that are retrieved using vector search are relevant that you wouldn't find in the legacy token based retrieval in health care. It's helping doctors and researchers across critical access, critical information faster and more accurately imagine being able to instantly find relevant medical records or research papers, even if they use slightly different terminologies. an example in e commerce, like I just mentioned, this can be life saving in emergency situations are crucial for groundbreaking medical research. And of course, all this is done while maintaining strict compliance with privacy regulations. VectorSearch is also a game changer for academia. It can analyze complex relationships between research papers, help us uncover hidden connections, and accelerate scientific discovery. For example, it can identify papers that are semantically similar. Even if they don't share any common citations, leading to new insights and collaborations. In the business world, it's breaking down information silos and making knowledge sharing a breeze. You don't need to dig through endless files and folders to find the right information. However, or regardless of how the information is stored or organized, the vector based indexes can provide a critical infrastructure or a critical system that can get you all these files and folders that you're looking for. This can lead to improved decision making, increased productivity, and a more connected workforce. Let's come to how this is implemented and being used in media and legal departments. In the media industry, VectorSearch is powering the next generation of content recommendation systems. It's like having a personal assistant who knows your taste in movies and TV shows better than you do, because it understands your intent, maps them into embeddings. It's helpful in suggesting content that you'll actually enjoy. It can analyze your viewing history, genre preferences, and even your emotional responses to content to provide highly personalized recommendations. For legal professionals, it's revolutionizing how they research case laws. Imagine being able to instantly find relevant precedents based on the nuances of legal arguments. even if they use different wording or address slightly different circumstances. this takes countless hours of research and leads to more efficient legal strategies. but how do we know if, it is actually better? the numbers across the industry from the research papers speak for themselves. We've seen significant improvements in search accuracy, user satisfaction across the board. In many cases, vector search has been shown to outperform traditional keyword searches by a significant margin, leading to more relevant results and a better user experience. The best part, this field is actively being worked upon. all the researchers across the industry are constantly refining this technology, making it even smarter and more efficient. There are technical challenges. as we deal with the ever growing amounts of data, we need to ensure our systems can handle the load. That means finding innovative ways to manage data and optimize performances, especially in distributed computing environments. things need to be fetched fast. performance optimization is another one. the model improvements are also happening. better and better models that can be used to create these embedding representations or semantic representations. They are continually being researched and innovated upon. These challenges actually also present exciting opportunities. Imagine combining vector search with cutting edge technologies like distributed learning. We could improve model performances while keeping data privacy in mind. Going on to the next slide. the future is bright. We're seeing a convergence with privacy preserving technologies, allowing us to search across decentralized databases without compromising sensitive information. there are applications in blockchains. There are applications in. um, privacy preserving medical databases that do not compromise sensitive information and are still able to find relevant information for your queries. We're also seeing integration with distributed computing, enabling robust search architectures that can handle massive amounts of data. With the rise of more sophisticated NLP, we can expect even deeper semantic understanding, leading to more accurate and relevant search results. Vector search is, more than just an incremental improvement. It's a paradigm shift in how we find information. It's about moving beyond simple keyword matching and truly understanding the meaning and context behind our searches. As we continue to refine the technology and explore new applications, it will definitely play a crucial role in unlocking the power and information and driving innovation across industries. Thank you, all for listening. I hope you're as excited about the future of vector search as I am. thank you so much for taking your time and hearing what I have to say. Thank you.